{smcl}
{com}{sf}{ul off}{txt}{.-}
       log:  {res}estimation.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 9 Jan 2011, 18:29:55

{com}. do "C:\Users\Piotr\AppData\Local\Temp\STD06000000.tmp"
{txt}
{com}. * Estimating models with established break dates
. 
. use IraqAll_all.dta", clear
{txt}
{com}. 
. tsset Quarter
{res}{txt}{col 9}time variable:  {res}{col 25}Quarter, 1968:1 to 2007:4
{txt}{col 17}delta:  {res}1 quarter
{txt}
{com}. 
. *** ALL COUNTRIES ***
. 
. ** mAll **
. * AR(3)
. * Estimated one break at 177 
. 
. qui gen D=0
{txt}
{com}. qui replace D=1 if Quarter>=177
{txt}
{com}. qui gen L1D=l.mAll*D
{txt}
{com}. qui gen L2D=l2.mAll*D
{txt}
{com}. qui gen L3D=l3.mAll*D
{txt}
{com}. regress mAll l.mAll l2.mAll l3.mAll D L1D L2D L3D, robust

{txt}Linear regression                                      Number of obs ={res}     157
                                                       {txt}F(  7,   149) ={res}   61.94
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.9339
                                                       {txt}Root MSE      = {res} 52.237

{txt}{hline 13}{c TT}{hline 64}
             {c |}               Robust
        mAll {c |}      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
{hline 13}{char +}{hline 64}
        mAll {c |}
         L1. {c |}  {res} .3748598   .1074441     3.49   0.001     .1625488    .5871707
         {txt}L2. {c |}  {res} .2553194   .0789712     3.23   0.002     .0992712    .4113676
         {txt}L3. {c |}  {res}  .068202   .1134609     0.60   0.549    -.1559982    .2924022
           {txt}D {c |}  {res} 182.1281   69.77034     2.61   0.010     44.26097    319.9952
         {txt}L1D {c |}  {res} .4238431   .2420474     1.75   0.082    -.0544457    .9021319
         {txt}L2D {c |}  {res}  .014284   .2201706     0.06   0.948    -.4207761    .4493441
         {txt}L3D {c |}  {res}-.4385284   .1815786    -2.42   0.017    -.7973301   -.0797266
       {txt}_cons {c |}  {res} 20.38034   5.369481     3.80   0.000     9.770176    30.99051
{txt}{hline 13}{c BT}{hline 64}

{com}. estat ic

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}    Obs    ll(null)   ll(model)     df          AIC         BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 17}  157{col 25}-1052.968{col 37}-839.7268{col 48}    8{col 57} 1695.454{col 69} 1719.903
{txt}{hline 13}{c BT}{hline 63}
{p 15 22 2}
Note:  N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}
{p_end}

{com}. *Getting rid of insignificant coefs
. regress mAll l.mAll l2.mAll D L1D L2D L3D, robust

{txt}Linear regression                                      Number of obs ={res}     157
                                                       {txt}F(  6,   150) ={res}   72.13
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.9338
                                                       {txt}Root MSE      = {res} 52.086

{txt}{hline 13}{c TT}{hline 64}
             {c |}               Robust
        mAll {c |}      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
{hline 13}{char +}{hline 64}
        mAll {c |}
         L1. {c |}  {res} .3942934   .1050039     3.76   0.000     .1868155    .6017713
         {txt}L2. {c |}  {res} .2821362   .0709349     3.98   0.000     .1419756    .4222968
           {txt}D {c |}  {res}  180.764   69.50754     2.60   0.010     43.42369    318.1043
         {txt}L1D {c |}  {res} .4044095   .2403225     1.68   0.094     -.070445     .879264
         {txt}L2D {c |}  {res}-.0125328    .216769    -0.06   0.954    -.4408479    .4157823
         {txt}L3D {c |}  {res}-.3703263   .1412921    -2.62   0.010    -.6495061   -.0911466
       {txt}_cons {c |}  {res} 21.74443   4.948623     4.39   0.000     11.96642    31.52244
{txt}{hline 13}{c BT}{hline 64}

{com}. * Ljung-Box statistic Q(4) - testing H0: white noise(in residuals)--> the first 4 autocorrelations are jointly insignificant
. predict double resid, residual
{txt}(3 missing values generated)

{com}. wntestq resid, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}    7.6638
{txt} Prob > chi2({res}4{txt})            = {res}    0.1047
{txt}
{com}. wntestq resid if D==0, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}    2.0087
{txt} Prob > chi2({res}4{txt})            = {res}    0.7342
{txt}
{com}. wntestq resid if D==1, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}    1.2124
{txt} Prob > chi2({res}4{txt})            = {res}    0.8761
{txt}
{com}. qui drop D L1D L2D L3D resid
{txt}
{com}. *Residuals are white noise
. 
. ** mCasualty **
. 
. * AR(3) 
. * Estimated break at 177
. 
. qui gen D=0
{txt}
{com}. qui replace D=1 if Quarter>=177
{txt}
{com}. qui gen L1D=l.mCasualty*D
{txt}
{com}. qui gen L2D=l2.mCasualty*D
{txt}
{com}. qui gen L3D=l3.mCasualty*D
{txt}
{com}. 
. regress mCasualty l.mCasualty l2.mCasualty l3.mCasualty D L1D L2D L3D, robust

{txt}Linear regression                                      Number of obs ={res}     157
                                                       {txt}F(  7,   149) ={res}   61.30
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.9439
                                                       {txt}Root MSE      = {res} 40.035

{txt}{hline 13}{c TT}{hline 64}
             {c |}               Robust
   mCasualty {c |}      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
{hline 13}{char +}{hline 64}
   mCasualty {c |}
         L1. {c |}  {res} .6135778   .1283356     4.78   0.000     .3599849    .8671707
         {txt}L2. {c |}  {res} .2212258   .1389076     1.59   0.113    -.0532575    .4957091
         {txt}L3. {c |}  {res} .0874767   .1128169     0.78   0.439    -.1354509    .3104043
           {txt}D {c |}  {res} 137.8144    50.8796     2.71   0.008     37.27566    238.3532
         {txt}L1D {c |}  {res} .2705052   .2782463     0.97   0.333    -.2793132    .8203236
         {txt}L2D {c |}  {res}-.0947417   .3479513    -0.27   0.786     -.782298    .5928146
         {txt}L3D {c |}  {res} -.359017   .2268705    -1.58   0.116    -.8073161    .0892821
       {txt}_cons {c |}  {res} 2.459329   1.588211     1.55   0.124    -.6789964    5.597655
{txt}{hline 13}{c BT}{hline 64}

{com}. 
. *Getting rid of insignificant coefs
. regress mCasualty l.mCasualty l2.mCasualty D L1D L2D, robust

{txt}Linear regression                                      Number of obs ={res}     158
                                                       {txt}F(  5,   152) ={res}   67.90
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.9390
                                                       {txt}Root MSE      = {res} 41.369

{txt}{hline 13}{c TT}{hline 64}
             {c |}               Robust
   mCasualty {c |}      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
{hline 13}{char +}{hline 64}
   mCasualty {c |}
         L1. {c |}  {res} .6382625   .1296153     4.92   0.000     .3821824    .8943426
         {txt}L2. {c |}  {res} .2654097    .105431     2.52   0.013     .0571103    .4737092
           {txt}D {c |}  {res}  130.761   49.03978     2.67   0.008     33.87344    227.6486
         {txt}L1D {c |}  {res} .3023988   .2516327     1.20   0.231    -.1947504     .799548
         {txt}L2D {c |}  {res}-.4418019    .228695    -1.93   0.055    -.8936332    .0100294
       {txt}_cons {c |}  {res} 2.826706   1.561144     1.81   0.072    -.2576369     5.91105
{txt}{hline 13}{c BT}{hline 64}

{com}. estat ic

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}    Obs    ll(null)   ll(model)     df          AIC         BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 17}  158{col 25}-1030.186{col 37}-809.2952{col 48}    6{col 57}  1630.59{col 69} 1648.966
{txt}{hline 13}{c BT}{hline 63}
{p 15 22 2}
Note:  N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}
{p_end}

{com}. * Ljung-Box statistic Q(4) - testing H0: white noise(in residuals)--> the first 4 autocorrelations are jointly insignificant
. predict double resid, residual
{txt}(2 missing values generated)

{com}. wntestq resid, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}   17.7779
{txt} Prob > chi2({res}4{txt})            = {res}    0.0014
{txt}
{com}. wntestq resid if D==0, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}    2.8424
{txt} Prob > chi2({res}4{txt})            = {res}    0.5845
{txt}
{com}. wntestq resid if D==1, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}    2.4784
{txt} Prob > chi2({res}4{txt})            = {res}    0.6485
{txt}
{com}. qui drop D L1D L2D L3D resid
{txt}
{com}. 
. 
. ** mUS_Target **
. 
. * AR(4) 
. * Estimated break at 177
. 
. qui gen D=0
{txt}
{com}. qui replace D=1 if Quarter>=177
{txt}
{com}. qui gen L1D=l.mUS_Target*D
{txt}
{com}. qui gen L2D=l2.mUS_Target*D
{txt}
{com}. qui gen L3D=l3.mUS_Target*D
{txt}
{com}. qui gen L4D=l4.mUS_Target*D
{txt}
{com}. regress mUS_Target l.mUS_Target l2.mUS_Target l3.mUS_Target l4.mUS_Target D L1D L2D L3D L4D, robust

{txt}Linear regression                                      Number of obs ={res}     156
                                                       {txt}F(  9,   146) ={res}   78.38
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.9509
                                                       {txt}Root MSE      = {res} 45.832

{txt}{hline 13}{c TT}{hline 64}
             {c |}               Robust
  mUS_Target {c |}      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
{hline 13}{char +}{hline 64}
  mUS_Target {c |}
         L1. {c |}  {res} .3962313   .2015824     1.97   0.051    -.0021651    .7946278
         {txt}L2. {c |}  {res} .3429157   .1208329     2.84   0.005     .1041081    .5817232
         {txt}L3. {c |}  {res}-.0226087   .1083366    -0.21   0.835    -.2367193     .191502
         {txt}L4. {c |}  {res} .1002905   .1048869     0.96   0.341    -.1070023    .3075833
           {txt}D {c |}  {res} 176.6087   68.74983     2.57   0.011     40.73531    312.4822
         {txt}L1D {c |}  {res} .4911452   .3142123     1.56   0.120    -.1298469    1.112137
         {txt}L2D {c |}  {res}-.1209865   .2601921    -0.46   0.643     -.635216     .393243
         {txt}L3D {c |}  {res}-.6416588   .3767968    -1.70   0.091     -1.38634     .103022
         {txt}L4D {c |}  {res} .1972732   .3235776     0.61   0.543    -.4422279    .8367743
       {txt}_cons {c |}  {res} 4.466127   2.641903     1.69   0.093    -.7551859     9.68744
{txt}{hline 13}{c BT}{hline 64}

{com}. estat ic

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}    Obs    ll(null)   ll(model)     df          AIC         BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 17}  156{col 25}-1048.044{col 37}-812.8833{col 48}   10{col 57} 1645.767{col 69} 1676.265
{txt}{hline 13}{c BT}{hline 63}
{p 15 22 2}
Note:  N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}
{p_end}

{com}. 
. *Getting rid of insignificant coefs
. regress mUS_Target l.mUS_Target D, robust

{txt}Linear regression                                      Number of obs ={res}     159
                                                       {txt}F(  2,   156) ={res}  122.11
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.9361
                                                       {txt}Root MSE      = {res} 50.681

{txt}{hline 13}{c TT}{hline 64}
             {c |}               Robust
  mUS_Target {c |}      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
{hline 13}{char +}{hline 64}
  mUS_Target {c |}
         L1. {c |}  {res} .7355318   .1031246     7.13   0.000      .531831    .9392326
           {txt}D {c |}  {res} 179.4594   67.73169     2.65   0.009     45.66984     313.249
       {txt}_cons {c |}  {res} 5.752688   2.292212     2.51   0.013     1.224909    10.28047
{txt}{hline 13}{c BT}{hline 64}

{com}. estat ic

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}    Obs    ll(null)   ll(model)     df          AIC         BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 17}  159{col 25}-1066.872{col 37}-848.2611{col 48}    3{col 57} 1702.522{col 69} 1711.729
{txt}{hline 13}{c BT}{hline 63}
{p 15 22 2}
Note:  N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}
{p_end}

{com}. regress mUS_Target l.mUS_Target l2.mUS_Target D L1D L2D, robust

{txt}Linear regression                                      Number of obs ={res}     158
                                                       {txt}F(  5,   152) ={res}   61.06
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.9374
                                                       {txt}Root MSE      = {res} 50.802

{txt}{hline 13}{c TT}{hline 64}
             {c |}               Robust
  mUS_Target {c |}      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
{hline 13}{char +}{hline 64}
  mUS_Target {c |}
         L1. {c |}  {res} .3970336   .1817885     2.18   0.030     .0378752     .756192
         {txt}L2. {c |}  {res} .3693353   .1126737     3.28   0.001     .1467265     .591944
           {txt}D {c |}  {res} 178.7148   70.57057     2.53   0.012     39.28893    318.1406
         {txt}L1D {c |}  {res}  .435374   .2857016     1.52   0.130     -.129085     .999833
         {txt}L2D {c |}  {res}-.4694076   .2266806    -2.07   0.040     -.917259   -.0215562
       {txt}_cons {c |}  {res} 5.388674   2.966725     1.82   0.071    -.4726664    11.25001
{txt}{hline 13}{c BT}{hline 64}

{com}. estat ic

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}    Obs    ll(null)   ll(model)     df          AIC         BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 17}  158{col 25}-1060.593{col 37}-841.7462{col 48}    6{col 57} 1695.492{col 69} 1713.868
{txt}{hline 13}{c BT}{hline 63}
{p 15 22 2}
Note:  N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}
{p_end}

{com}. 
. * Ljung-Box statistic Q(4) - testing H0: white noise(in residuals)--> the first 4 autocorrelations are jointly insignificant
. predict double resid, residual
{txt}(2 missing values generated)

{com}. wntestq resid if D==0, lags(8)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}    3.2526
{txt} Prob > chi2({res}8{txt})            = {res}    0.9175
{txt}
{com}. wntestq resid if D==1, lags(8)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}   11.0818
{txt} Prob > chi2({res}8{txt})            = {res}    0.1971
{txt}
{com}. qui drop D L1D L2D L3D L4D resid
{txt}
{com}. 
. 
{txt}
{com}. * Estimating models with known break dates: ONLY IRAQ (1968-2007)
. 
. *** IRAQ ONLY (including domestic)***
. 
. use MIPT_only Iraq ALL for regression.dta, clear
{txt}
{com}. drop  mFatal mInj mUSFatal mUSInj mUSCasualty
{txt}
{com}. 
. tsset Quarter
{res}{txt}{col 9}time variable:  {res}{col 25}Quarter, 1968:1 to 2007:4
{txt}{col 17}delta:  {res}1 quarter
{txt}
{com}. 
. ** mAll **
. * AR(4)
. * Estimated one break at 177 
. 
. qui gen D=0
{txt}
{com}. qui replace D=1 if Quarter>=177
{txt}
{com}. qui gen L1D=l.mAll*D
{txt}
{com}. qui gen L2D=l2.mAll*D
{txt}
{com}. qui gen L3D=l3.mAll*D
{txt}
{com}. qui gen L4D=l4.mAll*D
{txt}
{com}. regress mAll l.mAll l2.mAll l3.mAll l4.mAll D L1D L2D L3D L4D, robust

{txt}Linear regression                                      Number of obs ={res}     156
                                                       {txt}F(  9,   146) ={res}  195.48
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.9552
                                                       {txt}Root MSE      = {res} 44.557

{txt}{hline 13}{c TT}{hline 64}
             {c |}               Robust
        mAll {c |}      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
{hline 13}{char +}{hline 64}
        mAll {c |}
         L1. {c |}  {res} 1.794442   .1612822    11.13   0.000     1.475692    2.113191
         {txt}L2. {c |}  {res}-.5963637   .3087613    -1.93   0.055    -1.206583    .0138553
         {txt}L3. {c |}  {res}-.5183044   .2860704    -1.81   0.072    -1.083678    .0470696
         {txt}L4. {c |}  {res} .2240513   .2207817     1.01   0.312    -.2122897    .6603922
           {txt}D {c |}  {res} 180.0102   69.00703     2.61   0.010     43.62849     316.392
         {txt}L1D {c |}  {res}-.9059001   .2867453    -3.16   0.002    -1.472608   -.3391923
         {txt}L2D {c |}  {res} .8058363   .3845976     2.10   0.038     .0457384    1.565934
         {txt}L3D {c |}  {res}-.1529578   .4536408    -0.34   0.736    -1.049509    .7435932
         {txt}L4D {c |}  {res} .0936907   .3699265     0.25   0.800    -.6374118    .8247933
       {txt}_cons {c |}  {res} .2644922   .2550928     1.04   0.302    -.2396594    .7686439
{txt}{hline 13}{c BT}{hline 64}

{com}. estat ic

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}    Obs    ll(null)   ll(model)     df          AIC         BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 17}  156{col 25}-1050.634{col 37}-808.4827{col 48}   10{col 57} 1636.965{col 69} 1667.464
{txt}{hline 13}{c BT}{hline 63}
{p 15 22 2}
Note:  N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}
{p_end}

{com}. 
. *Getting rid of insignificant coef.
. regress mAll l.mAll l2.mAll D L1D L2D, robust

{txt}Linear regression                                      Number of obs ={res}     158
                                                       {txt}F(  5,   152) ={res}  132.00
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.9418
                                                       {txt}Root MSE      = {res} 49.795

{txt}{hline 13}{c TT}{hline 64}
             {c |}               Robust
        mAll {c |}      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
{hline 13}{char +}{hline 64}
        mAll {c |}
         L1. {c |}  {res} 1.848817   .1596303    11.58   0.000     1.533437    2.164198
         {txt}L2. {c |}  {res}-.8913842   .2472484    -3.61   0.000    -1.379871   -.4028971
           {txt}D {c |}  {res}  183.043   72.13538     2.54   0.012     40.52553    325.5604
         {txt}L1D {c |}  {res} -1.02107   .2719915    -3.75   0.000    -1.558442   -.4836982
         {txt}L2D {c |}  {res} .7954255   .3158117     2.52   0.013     .1714782    1.419373
       {txt}_cons {c |}  {res} .2387895    .220094     1.08   0.280    -.1960489    .6736279
{txt}{hline 13}{c BT}{hline 64}

{com}. 
. * Ljung-Box statistic Q(8) - testing H0: white noise(in residuals)--> the first 4 autocorrelations are jointly insignificant
. predict double resid, residual
{txt}(2 missing values generated)

{com}. wntestq resid, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}   41.9885
{txt} Prob > chi2({res}4{txt})            = {res}    0.0000
{txt}
{com}. wntestq resid if D==0, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}    8.4087
{txt} Prob > chi2({res}4{txt})            = {res}    0.0777
{txt}
{com}. wntestq resid if D==1, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}    5.4880
{txt} Prob > chi2({res}4{txt})            = {res}    0.2408
{txt}
{com}. qui drop D L1D L2D L3D L4D resid
{txt}
{com}. 
. *REJECTING NULL. Residuals are white noise
. 
. ** mCasualty **
. *AR(3)
. * Estimated one break at 177
. 
. qui gen D=0
{txt}
{com}. qui replace D=1 if Quarter>=177
{txt}
{com}. qui gen L1D=l.mCasualty*D
{txt}
{com}. qui gen L2D=l2.mCasualty*D
{txt}
{com}. qui gen L3D=l3.mCasualty*D
{txt}
{com}. qui gen L4D=l4.mCasualty*D
{txt}
{com}. regress mCasualty l.mCasualty l2.mCasualty l3.mCasualty D L1D L2D L3D, robust

{txt}Linear regression                                      Number of obs ={res}     157
                                                       {txt}F(  7,   149) ={res} 1421.47
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.9492
                                                       {txt}Root MSE      = {res} 38.425

{txt}{hline 13}{c TT}{hline 64}
             {c |}               Robust
   mCasualty {c |}      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
{hline 13}{char +}{hline 64}
   mCasualty {c |}
         L1. {c |}  {res} 1.932452   .2783722     6.94   0.000     1.382384    2.482519
         {txt}L2. {c |}  {res}-.7848928   .5222972    -1.50   0.135    -1.816959    .2471734
         {txt}L3. {c |}  {res}-.1583043   .2658051    -0.60   0.552    -.6835387    .3669301
           {txt}D {c |}  {res} 133.4241   50.53529     2.64   0.009     33.56566    233.2825
         {txt}L1D {c |}  {res}-1.027421   .3787682    -2.71   0.007    -1.775872   -.2789699
         {txt}L2D {c |}  {res} .8810301   .6147939     1.43   0.154    -.3338107    2.095871
         {txt}L3D {c |}  {res}-.0967071     .32712    -0.30   0.768    -.7431006    .5496864
       {txt}_cons {c |}  {res} .1566093   .1442222     1.09   0.279    -.1283756    .4415942
{txt}{hline 13}{c BT}{hline 64}

{com}. estat ic

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}    Obs    ll(null)   ll(model)     df          AIC         BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 17}  157{col 25}-1025.444{col 37}-791.5156{col 48}    8{col 57} 1599.031{col 69} 1623.481
{txt}{hline 13}{c BT}{hline 63}
{p 15 22 2}
Note:  N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}
{p_end}

{com}. regress mCasualty l.mCasualty l2.mCasualty D L1D L2D, robust

{txt}Linear regression                                      Number of obs ={res}     158
                                                       {txt}F(  5,   152) ={res} 1655.13
                                                       {txt}Prob > F      = {res} 0.0000
                                                       {txt}R-squared     = {res} 0.9451
                                                       {txt}Root MSE      = {res} 39.557

{txt}{hline 13}{c TT}{hline 64}
             {c |}               Robust
   mCasualty {c |}      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
{hline 13}{char +}{hline 64}
   mCasualty {c |}
         L1. {c |}  {res}  1.94687   .2645251     7.36   0.000     1.424249     2.46949
         {txt}L2. {c |}  {res}-.8597716    .454525    -1.89   0.060    -1.757774    .0382306
           {txt}D {c |}  {res} 127.3113   49.46338     2.57   0.011     29.58685    225.0358
         {txt}L1D {c |}  {res}-.9833165   .3466904    -2.84   0.005    -1.668271   -.2983625
         {txt}L2D {c |}  {res} .6663553   .5004876     1.33   0.185     -.322455    1.655166
       {txt}_cons {c |}  {res} .1341373   .1522892     0.88   0.380    -.1667395    .4350142
{txt}{hline 13}{c BT}{hline 64}

{com}. 
. * Ljung-Box statistic Q(3) - testing H0: white noise(in residuals)--> the first 4 autocorrelations are jointly insignificant
. predict double resid, residual
{txt}(2 missing values generated)

{com}. wntestq resid, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}   18.5795
{txt} Prob > chi2({res}4{txt})            = {res}    0.0010
{txt}
{com}. wntestq resid if D==0, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}    0.7892
{txt} Prob > chi2({res}4{txt})            = {res}    0.9399
{txt}
{com}. wntestq resid if D==1, lags(4)

{txt}Portmanteau test for white noise
{hline 39}
 Portmanteau (Q) statistic = {res}    2.3286
{txt} Prob > chi2({res}4{txt})            = {res}    0.6756
{txt}
{com}. qui drop D L1D L2D L3D L4D resid
{txt}
{com}. *REJECTING NULL. Residuals are white noise
. 
. 
{txt}end of do-file

{com}. log close
       {txt}log:  {res}estimation.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 9 Jan 2011, 18:33:31
{txt}{.-}
{smcl}
{txt}{sf}{ul off}